Epigenomic Tensor Predicts Disease Subtypes and Reveals Constrained Tumor Evolution

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (Decomposition and Classification of Epigenomic Tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences between tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic and prognostic strategies. Refer to individual Series

从复杂患者数据中解析疾病亚型的表观基因组演化与特异性，仍是生物医学领域的重大挑战。本研究提出DeCET（表观基因组张量分解与分类，Decomposition and Classification of Epigenomic Tensors）——一种可同时分析层级异质性数据的整合计算方法，用于精准识别不同组织类型、细胞分化状态及疾病亚型间的稳健表观基因组差异。将DeCET应用于本研究收集的21例子宫良性肿瘤（平滑肌瘤，leiomyoma）患者队列数据，成功鉴定出可区分正常子宫肌层与平滑肌瘤亚型的特异性表观基因组特征。平滑肌瘤存在远端增强子及长程组蛋白修饰的显著异常，且这些异常局限于限制病理表观基因组演化的染色质接触结构域内。此外，本研究通过多套涵盖不同癌症与细胞状态的公开表观基因组数据集，验证了DeCET的性能与优势。DeCET提取的表观基因组特征有助于加深我们对疾病状态、细胞发育与分化过程的理解，进而为未来的治疗、诊断及预后策略开发提供支撑。详见各独立数据集系列。